Update app.py

app.py

@@ -20,10 +20,20 @@ pipe = FluxPipeline.from_pretrained(base_model,
                                     vae=taef1,
                                     torch_dtype=torch.bfloat16)
 
-# Example usage:
-# Assuming 'image_list' is your list of PIL images and 'save_directory' is the folder where you want to save them
-paths = save_images_with_unique_filenames(image_list, 'output_images')
-print(paths)
+os.makedirs("frames", exist_ok=True)
+def save_images_with_unique_filenames(image_list, save_directory):
+    if not os.path.exists(save_directory):
+        os.makedirs(save_directory)
+
+    paths = []
+    for image in image_list:
+        unique_filename = f"{uuid.uuid4()}.png"  
+        file_path = os.path.join(save_directory, unique_filename)
+        image.save(file_path)
+        paths.append(file_path)
+    
+    return paths
+
 
 pipe.transformer.to(memory_format=torch.channels_last)
 # pipe.transformer = torch.compile(pipe.transformer, mode="max-autotune", fullgraph=True)
@@ -107,7 +117,6 @@ def generate(prompt,
     post_generation_slider_update = gr.update(label=comma_concepts_x, value=0, minimum=scale_min, maximum=scale_max, interactive=True)
     avg_diff_x = avg_diff.cpu()
     
-    os.makedirs("frames", exist_ok=True)
     image_paths = save_images_with_unique_filenames(images, "frames")
     
     return x_concept_1,x_concept_2, avg_diff_x, export_to_gif(images, "clip.gif", fps=5), canvas, image_paths, images[scale_middle], post_generation_slider_update, seed